Data-driven Causal Discovery for Pedestrians-Autonomous Personal Mobility Vehicle Interactions with eHMIs: From Psychological States to Walking Behaviors

2025-02-05

AI Summary

This study investigates the causal mechanisms by which pedestrians’ psychological states—specifically situation awareness, trust, risk perception, and decision hesitation—influence walking behavior during interactions with autonomous personal mobility vehicles (APMVs) equipped with external human–machine interfaces (eHMIs). Leveraging subjective questionnaire data and objective motion trajectory recordings from 42 participants, we systematically apply causal discovery methods—namely, the PC algorithm combined with structural equation modeling—to construct a multi-level psychological–behavioral causal graph. Results confirm that situation awareness, trust, and risk perception significantly affect decision hesitation and gait speed modulation, and identify a critical causal pathway: “trust → hesitation → gait adjustment.” The study both validates and extends the cognitive–decision–behavior theoretical framework, while providing interpretable, quantifiable design principles for eHMIs to enhance pedestrian interaction safety and user experience.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Reasoning under Uncertainty: Causality

Application Category

• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Autonomous personal mobility vehicle (APMV) is a new type of small smart vehicle designed for mixed-traffic environments, including interactions with pedestrians. To enhance the interaction experience between pedestrians and APMVs and to prevent potential risks, it is crucial to investigate pedestrians' walking behaviors when interacting with APMVs and to understand the psychological processes underlying these behaviors. This study aims to investigate the causal relationships between subjective evaluations of pedestrians and their walking behaviors during interactions with an external human-machine interface (eHMI) equipped with an APMV. An experiment of pedestrian-APMV interaction (N = 42) was conducted, in which various eHMIs on the APMV were designed to induce participants to experience different levels of subjective evaluations and generate the corresponding walking behaviors. Based on the hypothesized model of the pedestrian's cognition-decision-behavior process, the results of causal discovery align with the previously proposed model. Furthermore, this study further analyzes the direct and total causal effects of each factor and investigates the causal processes affecting several important factors in the field of human-vehicle interaction, such as situation awareness, trust in vehicle, risk perception, hesitation in decision making, and walking behaviors.

Problem

• Analyze pedestrian walking behavior with APMVs.
• Understand psychological states in human-vehicle interactions.
• Investigate causal effects of eHMIs on pedestrian behavior.

Innovation

• Data-driven causal discovery method
• External human-machine interface (eHMI)
• Psychological and behavioral interaction analysis